Source code for mmpretrain.models.backbones.resnest
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.cnn import build_conv_layer, build_norm_layer
from mmpretrain.registry import MODELS
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResLayer, ResNetV1d
class RSoftmax(nn.Module):
"""Radix Softmax module in ``SplitAttentionConv2d``.
Args:
radix (int): Radix of input.
groups (int): Groups of input.
"""
def __init__(self, radix, groups):
super().__init__()
self.radix = radix
self.groups = groups
def forward(self, x):
batch = x.size(0)
if self.radix > 1:
x = x.view(batch, self.groups, self.radix, -1).transpose(1, 2)
x = F.softmax(x, dim=1)
x = x.reshape(batch, -1)
else:
x = torch.sigmoid(x)
return x
class SplitAttentionConv2d(nn.Module):
"""Split-Attention Conv2d.
Args:
in_channels (int): Same as nn.Conv2d.
out_channels (int): Same as nn.Conv2d.
kernel_size (int | tuple[int]): Same as nn.Conv2d.
stride (int | tuple[int]): Same as nn.Conv2d.
padding (int | tuple[int]): Same as nn.Conv2d.
dilation (int | tuple[int]): Same as nn.Conv2d.
groups (int): Same as nn.Conv2d.
radix (int): Radix of SpltAtConv2d. Default: 2
reduction_factor (int): Reduction factor of SplitAttentionConv2d.
Default: 4.
conv_cfg (dict, optional): Config dict for convolution layer.
Default: None, which means using conv2d.
norm_cfg (dict, optional): Config dict for normalization layer.
Default: None.
"""
def __init__(self,
in_channels,
channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
radix=2,
reduction_factor=4,
conv_cfg=None,
norm_cfg=dict(type='BN')):
super(SplitAttentionConv2d, self).__init__()
inter_channels = max(in_channels * radix // reduction_factor, 32)
self.radix = radix
self.groups = groups
self.channels = channels
self.conv = build_conv_layer(
conv_cfg,
in_channels,
channels * radix,
kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups * radix,
bias=False)
self.norm0_name, norm0 = build_norm_layer(
norm_cfg, channels * radix, postfix=0)
self.add_module(self.norm0_name, norm0)
self.relu = nn.ReLU(inplace=True)
self.fc1 = build_conv_layer(
None, channels, inter_channels, 1, groups=self.groups)
self.norm1_name, norm1 = build_norm_layer(
norm_cfg, inter_channels, postfix=1)
self.add_module(self.norm1_name, norm1)
self.fc2 = build_conv_layer(
None, inter_channels, channels * radix, 1, groups=self.groups)
self.rsoftmax = RSoftmax(radix, groups)
@property
def norm0(self):
return getattr(self, self.norm0_name)
@property
def norm1(self):
return getattr(self, self.norm1_name)
def forward(self, x):
x = self.conv(x)
x = self.norm0(x)
x = self.relu(x)
batch, rchannel = x.shape[:2]
if self.radix > 1:
splits = x.view(batch, self.radix, -1, *x.shape[2:])
gap = splits.sum(dim=1)
else:
gap = x
gap = F.adaptive_avg_pool2d(gap, 1)
gap = self.fc1(gap)
gap = self.norm1(gap)
gap = self.relu(gap)
atten = self.fc2(gap)
atten = self.rsoftmax(atten).view(batch, -1, 1, 1)
if self.radix > 1:
attens = atten.view(batch, self.radix, -1, *atten.shape[2:])
out = torch.sum(attens * splits, dim=1)
else:
out = atten * x
return out.contiguous()
class Bottleneck(_Bottleneck):
"""Bottleneck block for ResNeSt.
Args:
in_channels (int): Input channels of this block.
out_channels (int): Output channels of this block.
groups (int): Groups of conv2.
width_per_group (int): Width per group of conv2. 64x4d indicates
``groups=64, width_per_group=4`` and 32x8d indicates
``groups=32, width_per_group=8``.
radix (int): Radix of SpltAtConv2d. Default: 2
reduction_factor (int): Reduction factor of SplitAttentionConv2d.
Default: 4.
avg_down_stride (bool): Whether to use average pool for stride in
Bottleneck. Default: True.
stride (int): stride of the block. Default: 1
dilation (int): dilation of convolution. Default: 1
downsample (nn.Module, optional): downsample operation on identity
branch. Default: None
style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
layer is the 3x3 conv layer, otherwise the stride-two layer is
the first 1x1 conv layer.
conv_cfg (dict, optional): dictionary to construct and config conv
layer. Default: None
norm_cfg (dict): dictionary to construct and config norm layer.
Default: dict(type='BN')
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed.
"""
def __init__(self,
in_channels,
out_channels,
groups=1,
width_per_group=4,
base_channels=64,
radix=2,
reduction_factor=4,
avg_down_stride=True,
**kwargs):
super(Bottleneck, self).__init__(in_channels, out_channels, **kwargs)
self.groups = groups
self.width_per_group = width_per_group
# For ResNet bottleneck, middle channels are determined by expansion
# and out_channels, but for ResNeXt bottleneck, it is determined by
# groups and width_per_group and the stage it is located in.
if groups != 1:
assert self.mid_channels % base_channels == 0
self.mid_channels = (
groups * width_per_group * self.mid_channels // base_channels)
self.avg_down_stride = avg_down_stride and self.conv2_stride > 1
self.norm1_name, norm1 = build_norm_layer(
self.norm_cfg, self.mid_channels, postfix=1)
self.norm3_name, norm3 = build_norm_layer(
self.norm_cfg, self.out_channels, postfix=3)
self.conv1 = build_conv_layer(
self.conv_cfg,
self.in_channels,
self.mid_channels,
kernel_size=1,
stride=self.conv1_stride,
bias=False)
self.add_module(self.norm1_name, norm1)
self.conv2 = SplitAttentionConv2d(
self.mid_channels,
self.mid_channels,
kernel_size=3,
stride=1 if self.avg_down_stride else self.conv2_stride,
padding=self.dilation,
dilation=self.dilation,
groups=groups,
radix=radix,
reduction_factor=reduction_factor,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg)
delattr(self, self.norm2_name)
if self.avg_down_stride:
self.avd_layer = nn.AvgPool2d(3, self.conv2_stride, padding=1)
self.conv3 = build_conv_layer(
self.conv_cfg,
self.mid_channels,
self.out_channels,
kernel_size=1,
bias=False)
self.add_module(self.norm3_name, norm3)
def forward(self, x):
def _inner_forward(x):
identity = x
out = self.conv1(x)
out = self.norm1(out)
out = self.relu(out)
out = self.conv2(out)
if self.avg_down_stride:
out = self.avd_layer(out)
out = self.conv3(out)
out = self.norm3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
return out
if self.with_cp and x.requires_grad:
out = cp.checkpoint(_inner_forward, x)
else:
out = _inner_forward(x)
out = self.relu(out)
return out
[docs]@MODELS.register_module()
class ResNeSt(ResNetV1d):
"""ResNeSt backbone.
Please refer to the `paper <https://arxiv.org/pdf/2004.08955.pdf>`__ for
details.
Args:
depth (int): Network depth, from {50, 101, 152, 200}.
groups (int): Groups of conv2 in Bottleneck. Default: 32.
width_per_group (int): Width per group of conv2 in Bottleneck.
Default: 4.
radix (int): Radix of SpltAtConv2d. Default: 2
reduction_factor (int): Reduction factor of SplitAttentionConv2d.
Default: 4.
avg_down_stride (bool): Whether to use average pool for stride in
Bottleneck. Default: True.
in_channels (int): Number of input image channels. Default: 3.
stem_channels (int): Output channels of the stem layer. Default: 64.
num_stages (int): Stages of the network. Default: 4.
strides (Sequence[int]): Strides of the first block of each stage.
Default: ``(1, 2, 2, 2)``.
dilations (Sequence[int]): Dilation of each stage.
Default: ``(1, 1, 1, 1)``.
out_indices (Sequence[int]): Output from which stages. If only one
stage is specified, a single tensor (feature map) is returned,
otherwise multiple stages are specified, a tuple of tensors will
be returned. Default: ``(3, )``.
style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
layer is the 3x3 conv layer, otherwise the stride-two layer is
the first 1x1 conv layer.
deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv.
Default: False.
avg_down (bool): Use AvgPool instead of stride conv when
downsampling in the bottleneck. Default: False.
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-1 means not freezing any parameters. Default: -1.
conv_cfg (dict | None): The config dict for conv layers. Default: None.
norm_cfg (dict): The config dict for norm layers.
norm_eval (bool): Whether to set norm layers to eval mode, namely,
freeze running stats (mean and var). Note: Effect on Batch Norm
and its variants only. Default: False.
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed. Default: False.
zero_init_residual (bool): Whether to use zero init for last norm layer
in resblocks to let them behave as identity. Default: True.
"""
arch_settings = {
50: (Bottleneck, (3, 4, 6, 3)),
101: (Bottleneck, (3, 4, 23, 3)),
152: (Bottleneck, (3, 8, 36, 3)),
200: (Bottleneck, (3, 24, 36, 3)),
269: (Bottleneck, (3, 30, 48, 8))
}
def __init__(self,
depth,
groups=1,
width_per_group=4,
radix=2,
reduction_factor=4,
avg_down_stride=True,
**kwargs):
self.groups = groups
self.width_per_group = width_per_group
self.radix = radix
self.reduction_factor = reduction_factor
self.avg_down_stride = avg_down_stride
super(ResNeSt, self).__init__(depth=depth, **kwargs)
def make_res_layer(self, **kwargs):
return ResLayer(
groups=self.groups,
width_per_group=self.width_per_group,
base_channels=self.base_channels,
radix=self.radix,
reduction_factor=self.reduction_factor,
avg_down_stride=self.avg_down_stride,
**kwargs)